{"id":"W3042453106","doi":"10.1002/smr.2276","title":"Towards reducing the time needed for load testing","year":2020,"lang":"en","type":"article","venue":"Journal of Software Evolution and Process","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Blackberry (Canada); Concordia University; University of Alberta; Queen's University","funders":"","keywords":"Workload; Computer science; Load testing; Metric (unit); Execution time; Response time; Test (biology); Reliability engineering; Work time; Performance metric; Test case; Real-time computing; Distributed computing; Operating system; Machine learning; Operations management; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002931258,0.001717268,0.0009810582,0.002164732,0.0006738751,0.001141307,0.003170509,0.0009984837,0.00457264],"category_scores_gemma":[0.02449799,0.0006552694,0.0007924684,0.000967167,0.001038725,0.001935337,0.001583883,0.001990008,0.001529594],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001319094,"about_ca_system_score_gemma":0.003506853,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004980747,"about_ca_topic_score_gemma":0.0056132,"domain_scores_codex":[0.9933065,0.002073584,0.0002972915,0.0006751925,0.003030018,0.0006174044],"domain_scores_gemma":[0.9667413,0.01796541,0.004155262,0.004316303,0.005334907,0.001486802],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00214027,0.002587289,0.03020879,0.0007138159,0.0002362204,0.0008380829,0.001057314,0.1511994,0.2125108,0.008665328,0.01032098,0.5795217],"study_design_scores_gemma":[0.0002965501,0.001555064,0.02012122,0.0001380798,0.0002302638,0.0006997528,0.0005489759,0.8722243,0.0810762,0.007009347,0.01601204,0.00008826183],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4422701,0.001952576,0.5307254,0.002476342,0.000375458,0.000472469,0.0004018323,0.01196327,0.009362519],"genre_scores_gemma":[0.8019091,0.0002240511,0.1929294,0.0003894176,0.0001253475,0.000253329,0.0005626404,0.001070017,0.002536598],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004980747,"threshold_uncertainty_score":0.01550215,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02455531795610353,"score_gpt":0.2602756308362111,"score_spread":0.2357203128801076,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}